Author:
Qasrawi Radwan,Hoteit Maha,Tayyem Reema,Bookari Khlood,Al Sabbah Haleama,Kamel Iman,Dashti Somaia,Allehdan Sabika,Bawadi Hiba,Waly Mostafa,Ibrahim Mohammed O.,De Backer Charlotte,Teunissen Lauranna,Van Royen Kathleen,Cuykx Isabelle,Decorte Paulien,Ouvrein Gaëlle,Poels Karolien,Vandebosch Heidi,Maldoy Katrien,Pabian Sara,Matthys Christophe,Smits Tim,Vrinten Jules,DeSmet Ann,Teughels Nelleke,Geuens Maggie,Vermeir Iris,Proesmans Viktor,Hudders Liselot,Al-Mannai Mariam,Alalwan Tariq,Naim Elissa,Mansour Rania,Yazbeck Nour,Agha Hazem,Seir Rania Abu,Arrish Jamila,Fallata Ghadir,Alhumaidan Omar,Alakeel Shihana,AlBuayjan Norah,Alkhunein Sarah,Binobaydan Budur,Alshaya Aeshah,Aldhaheri Ayesha,Polo Stephanny Vicuna,Al-Halawa Diala Abu,
Abstract
AbstractBackgroundA direct consequence of global warming, and strongly correlated with poor physical and mental health, food insecurity is a rising global concern associated with low dietary intake. The Coronavirus pandemic has further aggravated food insecurity among vulnerable communities, and thus has sparked the global conversation of equal food access, food distribution, and improvement of food support programs. This research was designed to identify the key features associated with food insecurity during the COVID-19 pandemic using Machine learning techniques. Seven machine learning algorithms were used in the model, which used a dataset of 32 features. The model was designed to predict food insecurity across ten Arab countries in the Gulf and Mediterranean regions. A total of 13,443 participants were extracted from the international Corona Cooking Survey conducted by 38 different countries during the COVID -19 pandemic.ResultsThe findings indicate that Jordanian, Palestinian, Lebanese, and Saudi Arabian respondents reported the highest rates of food insecurity in the region (15.4%, 13.7%, 13.7% and 11.3% respectively). On the other hand, Oman and Bahrain reported the lowest rates (5.4% and 5.5% respectively). Our model obtained accuracy levels of 70%-82% in all algorithms. Gradient Boosting and Random Forest techniques had the highest performance levels in predicting food insecurity (82% and 80% respectively). Place of residence, age, financial instability, difficulties in accessing food, and depression were found to be the most relevant features associated with food insecurity.ConclusionsThe ML algorithms seem to be an effective method in early detection and prediction of food insecurity and can profoundly aid policymaking. The integration of ML approaches in public health strategies could potentially improve the development of targeted and effective interventions to combat food insecurity in these regions and globally.
Publisher
Springer Science and Business Media LLC
Subject
Public Health, Environmental and Occupational Health
Reference54 articles.
1. Alinovi L, Mane E, Romano D. Towards the measurement of household resilience to food insecurity: applying a model to Palestinian household data. In Deriving food security information from national household budget surveys. Experiences, achievement, challenges (pp. 137-152). Food and Agricultural Organization of the United Nations. 2008.
2. Schroeder K, Smaldone A. Food insecurity: a concept analysis. Nurs Forum. 2015;50:274–84.
3. Flores HL, Amiri A. CE: addressing food insecurity in vulnerable populations. Am J Nurs. 2019;119:38–45.
4. de Araújo ML, Mendonça R de D, Lopes Filho JD, Lopes ACS. Association between food insecurity and food intake. Nutrition. 2018;54:54–9.
5. Asfahani F, Kadiyala S, Ghattas H. Food insecurity and subjective wellbeing among Arab youth living in varying contexts of political instability. J Adolesc Heal. 2019;64:70–8.
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
1. Assessing risk management trends in food security research;Environment, Development and Sustainability;2024-01-27